A comprehensive NLP-based voice assistant system for streamlined information retrieval in metro rail services of Bangladesh

Abstract

Bangladesh’s capital city Dhaka is served by the Dhaka Metro Rail. A metro-rail based rapid transit system is considered one of the important technologies that may decrease the working-hour wasting issue in a developed nation owing to traffic congestion. It moves between communities within an urban region or the cities that constitute a metropolitan area. However, to reap the advantages for both passengers and metro-rail authorities, a voice assistance system is also necessary for a metro-rail-based transit system. Many passengers expressed their dissatisfaction and frustration at the appearance of such difficulties from the very beginning of the metro-rail service. Many people complained about experiencing trouble obtaining tickets from vending machines due to technological challenges when the mass transit system was opened to the public. Officials reported that vending machines stopped operating as people attempted to use them without understanding how to use them. This research proposes a noble approach for the general population of Bangladesh. General people will be able to interact with a voice assistant and get their job done, such as collecting information about the train and metro-rail station. We will be undertaking our research with the help of Natural Language Processing (NLP) based on the Artificial Intelligence Markup Language (AIML) structure for training the model. The primary dataset creation procedure is cautiously defined, comprising question generation, response formulation, and category assignment. To ensure the relevance and accuracy of our dataset, a thorough verification procedure was done in collaboration with the Managing Director of Dhaka Mass Transit Company Limited (DMTCL). Term Frequency-Inverse Document Frequency (TF-IDF), and a sequential neural network model are trained with the dataset. We designed a web application with the capability to receive voice input and provide spoken output. This application was developed by utilizing a voice recognition Application Programming Interface (API) for voice-to-text and text-to-voice conversion. A closed domain question answering (cdQA) NLP solution was utilized to acquire information about the given query. The paper intends to show how voice assistants can be used in daily life in metro rail stations with minimal effort and to analyze if there is potential for making them accessible to the general public.